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Gender Classification Based on Multiscale Facial Fusion Feature

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  • Chunyu Zhang
  • Hui Ding
  • Yuanyuan Shang
  • Zhuhong Shao
  • Xiaoyan Fu

Abstract

For gender classification, we present a new approach based on Multiscale facial fusion feature (MS3F) to classify gender from face images. Fusion feature is extracted by the combination of Local Binary Pattern (LBP) and Local Phase Quantization (LPQ) descriptors, and a multiscale feature is generated through Multiblock (MB) and Multilevel (ML) methods. Support Vector Machine (SVM) is employed as the classifier to conduct gender classification. All the experiments are performed based on the Images of Groups (IoG) dataset. The results demonstrate that the application of Multiscale fusion feature greatly improves the performance of gender classification, and our approach outperforms the state-of-the-art techniques.

Suggested Citation

  • Chunyu Zhang & Hui Ding & Yuanyuan Shang & Zhuhong Shao & Xiaoyan Fu, 2018. "Gender Classification Based on Multiscale Facial Fusion Feature," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-6, November.
    • Handle: RePEc:hin:jnlmpe:1924151
    DOI: 10.1155/2018/1924151
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